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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

NSUF BOILER Pre-Irradiation Characterization and High Flux Isotope Reactor Experiment Design

Alumina-forming austenitic (AFA) stainless steels have emerged as a candidate alloy because of their high-temperature strength, formability, cost, and compatibility with primary coolants for lead-cooled fast reactors (LFRs). This class of steels has exceptional high-temperature oxidation performance; however, a high concentration of Ni is required to stabilize the austenite phase and to provide sufficient creep strength. AFA stainless steels are also susceptible to liquid metal embrittlement (LME). Additionally, under neutron irradiation, Ni will enrich at grain boundaries due to radiation-induced segregation (RIS). Nickel RIS can increase the LME under these coupled effects. Oak Ridge National Laboratory (ORNL) and the NSUF program have leveraged its High Flux Isotope Reactor (HFIR) and experience with complex irradiation experiments to design experiment capsules that test the aforementioned coupled effects. These capsules are designed for insertion in the central flux trap, the highest flux region, of HFIR. The experiment capsules will be filled with Pb, designed to passively melt from the gamma heating in HFIR. The specimens were fabricated into miniature tensile specimens from two different alloys, GA05-25Ni and GA05-20Ni, varying Ni concentrations. The experiment capsules are designed to achieve target temperatures of 400 °C and 650 °C with accumulated dosage of 3 dpa. This report documents the specimen alloy characterization, experimental design, and expected performance of the capsules.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Intrusive Uncertainty Quantification and Optimal Experiment Design in the Open-Source Pyomo Ecosystem

This contribution describes ParmEst and Pyomo.DoE, two pillars of the open-source Python-based Pyomo ecosystem for computational optimization with (partial differential) algebraic equation mathematical models. Specifically, ParmEst facilitates intrusive frequentist parameter estimation (PE) and uncertainty quantification (UQ) through built-in features, such as covariance matrix estimation, bootstrapping, and likelihood ratio tests. Complementary, Pyomo.DoE enables optimal experiment design by maximizing various metrics of the Fisher information matrix, such as A-optimality (trace), D-optimality (determinant), E-optimality (minimum eigenvalue), and ME-optimality (condition number). ParmEst and Pyomo.DoE can solve high-dimensional optimization problems by leveraging the model structure and exact derivative information. Finally, we will discuss future opportunities to integrate PE and UQ capabilities with optimization under uncertainty, including robust optimization with non-convex models via PyROS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

IER 516: Zirconium Test Assembly CED Phase-1: Preliminary Experiment Design

IER-516, Zirconium Test Assembly (ZTA), is a campaign to design, execute, and document a series of high-fidelity critical benchmark experiments to validate current and future zirconium (Zr) and zirconium hydride (ZrH x ) nuclear data evaluations. ZTA is a collaborative project between Los Alamos National Laboratory and the French Autoritè de Sûretè Nuclèaire et de Radioprotection. The experiments will be fueled with highly enriched uranium (HEU), and utilize the Comet critical assembly machine at the National Criticality Experiments Research Center. A total of ten preliminary critical experiments were designed and optimized for Zr and ZrHx nuclear data sensitivities using the MCNP-Particle Swarm Optimization methodology in the thermal, epithermal, intermediate, and fast neutron energy regions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Thermal Gradient and Neutron Irradiation Experiment Design for Fusion Reactor Materials in the Advanced Test Reactor

This work outlines a hypothetical coupled thermal gradient and neutron irradiation experiment in the Advanced Test Reactor (ATR) at the Idaho National Laboratory. Although the ATR is a thermal spectrum test reactor and doesn’t inherently produce a flux spectrum dominated by the high-energy neutrons typical in a fusion reactor, it’s multitude of experiment positions and dynamic flux environment make it a suitable platform for investigating fusion related issues.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Loss of Flow Conditions in a Modern Pool-type SFR and In-Pile Experiment Design

The metallic fuel safety performance under unprotected design-basis transients is a key consideration for the deployment of advanced sodium fast reactors (SFRs). Reliable data are needed to validate advanced safety codes, reduce uncertainty in cladding failure thresholds, and strengthen confidence in licensing approaches. To address this need, this report develops blueprints for a conceptual sodium loss-of-flow (LOF) experiment in the Mk-IIIR loop at the Transient Reactor Test Facility (TREAT), providing the technical foundation for future integral testing.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Optimizing Batch Crystallization with Model-based Design of Experiments

Poster for Foundations of Computer Aided Process Design (FOCAPD) 2024 describing the formality of model-based design of experiments for batch crystallization. This paper focuses on the implementation of model-based design of experiments for parameter precision, enabled by Pyomo.DoE, a package in Python. Specifically, this work outlines an identifiability analysis for the estimability of the model parameters in a batch crystallization system.

Lynch, Hailey↗
DOE OSTI2024

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

MINE: a new way to design genetics experiments for discovery

Abstract The Maximally Informative Next Experiment or MINE is a new experimental design approach for experiments, such as those in omics, in which the number of effects or parameters p greatly exceeds the number of samples n (p > n). Classical experimental design presumes n > p for inference about parameters and its application to p > n can lead to over-fitting. To overcome p > n, MINE is an ensemble method, which makes predictions about future experiments from an existing ensemble of models consistent with available data in order to select the most informative next experiment. Its advantages are in exploration of the data for new relationships with n < p and being able to integrate smaller and more tractable experiments to replace adaptively one large classic experiment as discoveries are made. Thus, using MINE is model-guided and adaptive over time in a large omics study. Here, MINE is illustrated in two distinct multiyear experiments, one involving genetic networks in Neurospora crassa and a second one involving a genome-wide association study in Sorghum bicolor as a comparison to classic experimental design in an agricultural setting.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Neutron irradiation & thermomechanical experiment (NITE) - design

For the reliable long-term operation of fusion power plants, it is crucial to understand and predict the lifetime of materials in use. These materials include all structural and functional materials utilized at the first wall, blanket, magnets, and shielding. The key challenge is, that the harsh environment including high heat fluxes, high thermal stress and stress cycling, neutron irradiation, and sputtering on such materials should not be viewed separately. Currently, the synergistic loads cannot be evaluated experimentally because of the lack of adequate facilities. The purpose of that work is to design a synergetic Neutron Irradiation and Thermomechanical Experiment (NITE) for fusion materials. This design will leverage the existing Advanced-Test-Reactor (ATR), a fission reactor at the Idaho National Laboratory. We also acknowledge that with existing fission reactors the exact fusion condition cannot be created, and the limitations are critically discussed. The combination of neutron irradiation with a high heat flux is the focus. This is realized with an irradiation capsule design that includes a TRISO fueled region inside the capsule to enable a steady-state heat flux on one side of the specimen. In conclusion, the experimental design modeling showed that steady-state heat fluxes of 2.4 MW/m 2 with a thermal gradient of above 250°C can be achieved in a 5 mm thick specimen.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

DAMSA Experiment Conceptual Design White Paper

DAMSA (DArk Messenger Searches at an Accelerator) is a novel short-baseline accelerator experiment aimed at probing short-lived physics processes, including searches for evidence of a dark sector of particle physics and well-motivated Standard Model signals. Motivated by open questions in neutrino physics and the absence of conclusive evidence for conventional weakly interacting massive particles, DAMSA targets MeV-to-sub-GeV dark-sector messengers with feeble couplings that can be produced in abundance at the PIP-II LINAC. By employing an ultra-short baseline of order one meter, DAMSA is uniquely positioned to overcome the beam-dump "ceiling" that limits sensitivity to promptly decaying particles in longer-baseline experiments. The conceptual design emphasizes a beam-dump production scheme combined with a compact detector optimized for rare decays while mitigating intense neutron-induced backgrounds inherent to high-power proton beams. To validate the experimental strategy and detector technologies, the Little DAMSA Path-Finder (LDPF) proof-of-concept experiment is proposed, focusing on axion-like particles decaying to two photons and operating with 300 MeV electron beams at FAST. Successful realization of LDPF will establish the feasibility of the DAMSA approach, enabling a broad and powerful program to explore short-lived new physics and precision Standard Model processes in a previously inaccessible regime. This conceptual design document outlines the technical details of DAMSA's physics goals, the beam facility proposals, key experimental challenges and how to overcome them, and the proposed experimental staging campaigns.

Bhattarai, Prithak [Texas U., Arlington]↗
DOE OSTI2024

Advancing Insights into Electrochemical Pre‐Treatments of Supported Nanoparticle Electrocatalysts by Combining a Design of Experiments Strategy with In Situ Characterization

Activation, break-in, and/or pre-treatment protocols are generally applied to energy conversion devices before regular operation to reach stable performance. There remains much to understand about the relationships among physical properties, performance, and electrochemical pre-treatments. Here, a design-of-experiments (DoE) strategy is employed to address this gap by demonstrating the influence of five pre-treatment parameters for carbon-supported Pt-nanoparticle catalysts on the electrocatalytic oxygen reduction reaction (ORR). A subset of pre-treatments, developed using a central composite design, are tested in a flow cell combined with an inductively-coupled plasma mass spectrometer (on-line ICP-MS). The DoE-based approach facilitates comprehensive insights from two orders of magnitude fewer experiments than a conventional grid search. The coupled on-line ICP-MS setup enables effective catalysis and real-time catalyst dissolution data. Leveraging insights from DoE for on-line ICP-MS and additional characterization, a model is built between the degradation of a multi-dimensional supported Pt surface, its performance, and applied electrochemical parameters. These investigations identify surface modifications, such as oxidation, and subsequent restructuring of Pt during pre-treatment as a primary cause of performance deterioration during ORR. By combining DoE with advanced characterization techniques, a powerful approach is demonstrated to gain a mechanistic understanding of pre-treatment protocols that can be broadly adapted to various reaction chemistries.

Platinum↗
DOE OSTI2024

Optimizing Batch Crystallization with Model-based Design of Experiments

Adaptive and self-optimizing intelligent systems such as digital twins are increasingly important in science and engineering. Digital twins utilize mathematical models to provide added precision to decision-making. However, physics-informed models are challenging to build, calibrate, and validate with existing data science methods. Model-based design of experiments (MBDoE) is a popular framework for optimizing data collection to maximize parameter precision in mathematical models and digital twins. In this work, we apply MBDoE, facilitated by the open-source package Pyomo.DoE, to train and validate mathematical models for batch crystallization. We quantitatively examined the estimability of the model parameters for experiments with different cooling rates. This analysis provides a quantitative explanation for the heuristic of using multiple experiments at different cooling rates.

Lynch, Hailey↗